Study of transductive learning and unsupervised feature construction methods for biological sequence classification

Next Generation Sequencing (NGS) technologies have led to fast and inexpensive production of large amounts of biological sequence data, including nucleotide sequences and derived protein sequences. These fast-increasing volumes of data pose challenges to computational methods for annotation. Machine learning approaches, primarily supervised algorithms, have been widely used to assist with classification tasks in bioinformatics. However, supervised algorithms rely on large amounts of labeled data in order to produce quality predictors. Oftentimes, labeled data is difficult and expensive to acquire in sufficiently large quantities. When only limited amounts of labeled data but considerably larger amounts of unlabeled data are available for a specific annotation problem, semi-supervised learning approaches represent a cost-effective alternative. In this work, we focus on a special case of semi-supervised learning, namely transductive learning, in which the algorithm has access during the training phase to the instances that need to be labeled. Transduction is particularly suitable for biological sequence classification, where the goal is generally to label a given set of unlabeled instances. However, a challenge that needs to be addressed in this context consists of identification of compact sets of informative features. Given the lack of labeled data, standard supervised feature selection methods may result in unreliable features. Therefore, we study recently proposed unsupervised feature construction approaches together with transductive learning. Experimental results on two classification problems, namely cassette exon identification and protein localization, show that the unsupervised features result in better performance than the supervised features.